"... We present a variational integration of nonlinear shape statistics into a Mumford-Shah based segmentation process. The nonlinear statistics are derived from a set of training silhouettes by a novel method of density estimation which can be considered as an extension of kernel PCA to a stochastic fra ..."

We present a variational integration of nonlinear shape statistics into a Mumford-Shahbasedsegmentation process. The nonlinear statistics are derived from a set of training silhouettes by a novel method of density estimation which can be considered as an extension of kernel PCA to a stochastic

"... In this work we revisit the Mumford-Shah functional, one of the most studied variational approaches to image segmentation. The contribution of this paper is to propose an algorithm which allows to minimize a convex relaxation of the Mumford-Shah functional obtained by functional lifting. The algorit ..."

In this work we revisit the Mumford-Shah functional, one of the most studied variational approaches to imagesegmentation. The contribution of this paper is to propose an algorithm which allows to minimize a convex relaxation of the Mumford-Shah functional obtained by functional lifting

"... We propose a new multiphase level set framework for image segmentation using the Mumford and Shah model, for piecewise constant and piecewise smooth optimal approximations. The proposed method is also a generalization of an active contour model without edges based 2-phase segmentation, developed by ..."

We propose a new multiphase level set framework for imagesegmentation using the Mumford and Shahmodel, for piecewise constant and piecewise smooth optimal approximations. The proposed method is also a generalization of an active contour model without edges based 2-phase segmentation, developed

"... We present a simple and robust feature preserving image regularization by letting local region measures to modulate the diffusivity. The purpose of this modulation is to disambiguate low level cues in early both gray and color natural images demonstrate the potential of the method under difficult no ..."

"... This report compares three algorithms for segmentation of synthetic aperture radar (SAR) imagery with a new algorithm called the full λ-schedule that is an extension of the algorithm by Koepfler et al. based on Mumford-Shah functionals. We have eliminated the need to select λ values from the Koepfle ..."

This report compares three algorithms for segmentation of synthetic aperture radar (SAR) imagery with a new algorithm called the full λ-schedule that is an extension of the algorithm by Koepfler et al. based on Mumford-Shah functionals. We have eliminated the need to select λ values from

"... Abstract—This paper is concerned with an improved algorithm based on the piecewise-smooth Mumford and Shah (MS) functional for an efficient and reliable segmentation. In order to speed up convergence, an additional force, at each time step, is introduced further to drive the evolution of the curves ..."

Abstract—This paper is concerned with an improved algorithm based on the piecewise-smooth Mumford and Shah (MS) functional for an efficient and reliable segmentation. In order to speed up convergence, an additional force, at each time step, is introduced further to drive the evolution of the curves

"... We propose an efficient algorithm for minimizing the piecewise constant Mumford-Shah functional of image segmentation. It is based on the threshold dynamics of Merriman, Bence, and Osher for evolving an interface by its mean curvature. We show that a very fast minimization can be achieved by alterna ..."

We propose an efficient algorithm for minimizing the piecewise constant Mumford-Shah functional of imagesegmentation. It is based on the threshold dynamics of Merriman, Bence, and Osher for evolving an interface by its mean curvature. We show that a very fast minimization can be achieved

"... We present a modification of the Mumford-Shah functional and its cartoon limit which facilitates the incorporation of a statistical prior on the shape of the segmenting contour. By minimizing a single energy functional, we obtain a segmentation process which maximizes both the grey value homogeneit ..."

We present a modification of the Mumford-Shah functional and its cartoon limit which facilitates the incorporation of a statistical prior on the shape of the segmenting contour. By minimizing a single energy functional, we obtain a segmentation process which maximizes both the grey value

"... In contemporary image and vision analysis, stochastic approaches demonstrate great flexibility in representing and modeling complex phenomena, while variational-PDE methods gain enormous computational advantages over Monte Carlo or other stochastic algorithms. In combination, the two can lead to muc ..."

to much more powerful novel models and efficient algorithms. In the current work, we propose a stochastic-variationalmodel for soft (or fuzzy) Mumford-Shahsegmentation of mixture image patterns. Unlike the classical hard Mumford-Shahsegmentation, the new model allows each pixel to belong to each image